Quantitative research scientist working across nutrition, education, public health, biostatistics, epidemiology, and program evaluation.
With a PhD in Nutritional Sciences, I bring substantive training in nutrition together with experience across education, public health, and applied evaluation. I turn complex administrative, longitudinal, and survey data into evidence that is transparent enough to audit and practical enough to use. My portfolio emphasizes defensible estimands, diagnostic checks, reproducible code, careful uncertainty, and clear boundaries between association, prediction, and causation.
| Project | Question and methods |
|---|---|
| NHANES nutrition survey analysis | Public two-day dietary recalls, energy-adjusted fiber and sodium density, dietary weights, domain estimates, Taylor-linearized uncertainty, missing-data flow, and survey-weighted regression |
| Nutrition epidemiology case study | Two-day dietary-recall averaging, energy-adjusted fiber and sodium density, completeness reporting, descriptive group contrasts, uncertainty, and measurement-error boundaries |
| Public Health Methods Lab | Nutrition epidemiology, respiratory-disease surveillance, direct age standardization, outbreak risk ratios, rolling signals, Kaplan-Meier analysis, automated tests, and generated outputs |
| Quasi-experimental program evaluation | Propensity-score matching and weighting, common-support and balance diagnostics, clustered inference, regression adjustment, and sensitivity across estimators |
| Multilevel outcomes analysis | Three-level longitudinal modeling, variance decomposition, random effects, contextual variation, interactions, and residual diagnostics |
| Structural equation modeling | Confirmatory factor analysis, measurement invariance, FIML, latent-variable mediation, model diagnostics, and careful noncausal interpretation |
Featured nutritional-epidemiology project: The NHANES Nutrition Survey Analysis uses deidentified CDC public-use data to demonstrate two-day dietary measurement, complex-survey inference, missing-data reporting, and descriptive regression.
The Public Health Methods Lab complements it with synthetic, reproducible surveillance, outbreak, nutrition, and time-to-event examples.
- Breakfast consumption in low-income Hispanic elementary school-aged children — first-author cross-sectional study of anthropometric, metabolic, and dietary parameters
- Impact of a school-based gardening, cooking, and nutrition intervention on diet intake and quality — TX Sprouts cluster randomized controlled trial
- Design and participant characteristics of TX Sprouts — intervention design and cohort profile
My persistent researcher identifier is ORCID 0000-0002-1140-3185. The complete public publication list is available through My NCBI Bibliography, with an additional profile on ResearchGate.
| Project | What reviewers can inspect |
|---|---|
| Administrative data pipeline | Multisource standardization, deduplication, joins, audit trails, reproducible R/Stata workflows, and validation tests |
| Evaluation data-quality toolkit | Data contracts, domain/range and cross-field rules, issue-level audit output, reusable SQL checks, and CI |
| Student success predictive modeling | Temporal validation, calibration, capacity-aware thresholds, subgroup diagnostics, model cards, and human-review controls |
| Student success operations dashboard | SQL metric layer, dimensional modeling, Power BI-ready measures, implementation monitoring, tested KPIs, and an executive decision memo |
| SQL analytics case study | CTEs, windows, cohorts, anomaly review, tested outputs, metric documentation, and decision-ready interpretation |
- Start with the decision and estimand. Define the population, comparison, outcome, time window, and interpretation before fitting a model.
- Make validity visible. Surface missingness, data quality, balance, calibration, clustering, uncertainty, subgroup behavior, and model assumptions.
- Build for reproduction. Use deterministic synthetic data, executable workflows, tests, continuous integration, data dictionaries, and saved reference outputs.
- Communicate limits clearly. Separate descriptive, predictive, associational, and causal claims; keep privacy and responsible-use constraints close to the results.
Nutrition, epidemiology, and biostatistics: dietary recall analysis, energy adjustment, surveillance rates, direct standardization, cohort measures, time-to-event analysis, causal inference, longitudinal and multilevel models, measurement models, missing-data methods, uncertainty and sensitivity analysis
Analysis: R, Stata, Python, SQL
Data and reporting: AWS Athena, Power BI; additional exposure to Tableau, Snowflake, and reproducible Quarto reporting
Delivery: Git, GitHub Actions, automated tests, data contracts, model cards, decision memos, and research governance
Project leadership: research operations, stakeholder engagement, scope and risk management; PMP certification expected August 2026
Portfolio projects use either deterministic synthetic records or explicitly documented deidentified public-use data. No client, student, patient, protected health information, restricted records, or row-level public-use files are republished. Each project is designed to expose the full workflow—assumptions, code, quality checks, outputs, interpretation, and limitations—rather than only a polished final chart.